r - 世界地图与 ggmap

Question

我正在使用 ggmap，希望有一张以澳大利亚为中心的世界地图，我可以轻松地绘制地理编码点。与其他一些 map 包相比，ggmap 似乎更容易使用。然而，当我使用下面的代码浏览 map 时，它会出错。

gc <- geocode('australia')
center <- as.numeric(gc) 
> map <- get_map(location = center, source="google", maptype="terrain", zoom=0)
Error: zoom must be a whole number between 1 and 21

来自 get_map 帮助:"zoom: map 缩放，从0(全世界)到21(建筑物)的整数，默认值10(城市)。openstreetmaps限制缩放为18，花蕊 map 的限制取决于 map 类型。‘auto’自动确定边界框规范的缩放，并且中心/缩放规范默认为 10。”

将缩放更改为 1 对于 get_map 不会出错，但对于绘制该 map 会出错

map <- get_map(location = center, source="google", maptype="terrain", zoom=1)
ggmap(map)

Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf

看起来经度没有被拉过。最后，缩放为 2 时，它确实可以工作，但无法显示整个世界的 map

所以，我的问题是如何使用 get_map 来获取世界地图？

session 信息:

sessionInfo() R version 2.15.0 (2012-03-30) Platform: i386-pc-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] mapproj_1.1-8.3 maps_2.2-6      rgdal_0.7-12    sp_0.9-99      
[5] ggmap_2.1       ggplot2_0.9.1  

loaded via a namespace (and not attached):
[1] colorspace_1.1-1   dichromat_1.2-4    digest_0.5.2       grid_2.15.0       
[5] labeling_0.1       lattice_0.20-6     MASS_7.3-17        memoise_0.1       
[9] munsell_0.3        plyr_1.7.1         png_0.1-4          proto_0.3-9.2     
[13] RColorBrewer_1.0-5 reshape2_1.2.1     RgoogleMaps_1.2.0  rjson_0.2.8       
[17] scales_0.2.1       stringr_0.6        tools_2.15.0 

Answer 1

编辑:更新至 ggplot2 v 0.9.3

我最近尝试了类似的方法，但收效甚微。然而，有多种方法可以使世界地图以 map 为中心。封装:见here , here ，和here 。使用后者的代码，下面的示例将世界地图以经度 160 为中心，在使用 ggplot2 绘制的世界地图上绘制 CRAN 镜像位置(使用 ggmap 包中的 geocode() 函数获得的坐标)，并为新西兰绘制颜色(使用geom_polygon)。将 map 以经度 160 为中心，使整个非洲位于 map 左侧，格陵兰岛大部分地区位于 map 右侧。

library(maps)
library(plyr)
library(ggplot2)
library(sp)
library(ggmap)

# Get some points to plot - CRAN Mirrors
Mirrors = getCRANmirrors(all = FALSE, local.only = FALSE)

Mirrors$Place = paste(Mirrors$City, ", ", Mirrors$Country, sep = "")    # Be patient
tmp = geocode(Mirrors$Place)
Mirrors = cbind(Mirrors, tmp)

###################################################################################################
# Recentre worldmap (and Mirrors coordinates) on longitude 160
### Code by Claudia Engel  March 19, 2012, www.stanford.edu/~cengel/blog

### Recenter ####
center <- 160 # positive values only

# shift coordinates to recenter CRAN Mirrors
Mirrors$long.recenter <- ifelse(Mirrors$lon < center - 180 , Mirrors$lon + 360, Mirrors$lon)

# shift coordinates to recenter worldmap
worldmap <- map_data ("world")
worldmap$long.recenter <- ifelse(worldmap$long < center - 180 , worldmap$long + 360, worldmap$long)

### Function to regroup split lines and polygons
# Takes dataframe, column with long and unique group variable, returns df with added column named group.regroup
RegroupElements <- function(df, longcol, idcol){
  g <- rep(1, length(df[,longcol]))
  if (diff(range(df[,longcol])) > 300) { # check if longitude within group differs more than 300 deg, ie if element was split
    d <- df[,longcol] > mean(range(df[,longcol])) # we use the mean to help us separate the extreme values
    g[!d] <- 1 # some marker for parts that stay in place (we cheat here a little, as we do not take into account concave polygons)
    g[d] <- 2 # parts that are moved
  }
  g <- paste(df[, idcol], g, sep=".") # attach to id to create unique group variable for the dataset
  df$group.regroup <- g
  df
}

### Function to close regrouped polygons
# Takes dataframe, checks if 1st and last longitude value are the same, if not, inserts first as last and reassigns order variable
ClosePolygons <- function(df, longcol, ordercol){
  if (df[1,longcol] != df[nrow(df),longcol]) {
    tmp <- df[1,]
    df <- rbind(df,tmp)
  }
  o <- c(1: nrow(df)) # rassign the order variable
  df[,ordercol] <- o
  df
}

# now regroup
worldmap.rg <- ddply(worldmap, .(group), RegroupElements, "long.recenter", "group")

# close polys
worldmap.cp <- ddply(worldmap.rg, .(group.regroup), ClosePolygons, "long.recenter", "order") # use the new grouping var
#############################################################################

# Plot worldmap using data from worldmap.cp
windows(9.2, 4)
worldmap = ggplot(aes(x = long.recenter, y = lat), data = worldmap.cp) + 
  geom_polygon(aes(group = group.regroup), fill="#f9f9f9", colour = "grey65") + 
  scale_y_continuous(limits = c(-60, 85)) + 
  coord_equal() +  theme_bw() + 
  theme(legend.position = "none",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    #axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(), 
    panel.border = element_rect(colour = "black"))

# Plot the CRAN Mirrors
worldmap = worldmap + geom_point(data = Mirrors, aes(long.recenter, lat),
   colour = "red", pch = 19, size = 3, alpha = .4)

# Colour New Zealand
# Take care of variable names in worldmap.cp
head(worldmap.cp)
worldmap + geom_polygon(data = subset(worldmap.cp, region == "New Zealand", select = c(long.recenter, lat, group.regroup)), 
          aes(x = long.recenter, y = lat, group = group.regroup), fill = "blue")